▷ (Immediate Start) Senior Principal consultant- MLOps Engineer

2 weeks ago

Mumbai, India Genpact Full time

Job Description

Inviting applications for the role of Senior Principal Consultant - ML Engineers

In this role, lead the automation and orchestration of our machine learning infrastructure and CI/CD pipelines on public cloud (preferably AWS). This role is essential for enabling scalable, secure, and reproducible deployments of both classical AI/ML models and Generative AI solutions in production environments.

Responsibilities

.Develop and maintain CI/CD pipelines for AI/GenAI models on AWS using GitHub Actions and CodePipeline. (Not Limited to)

.Automate infrastructure provisioning using IAC. (Terraform, Bicep Etc)

.Any cloud platform- Azure or AWS

.Package and deploy AI/GenAI models on (SageMaker, Lambda, API Gateway).

.Write Python scripts for automation, deployment, and monitoring.

.Engaging in the design, development and maintenance of data pipelines for various AI use cases

.Active contribution to key deliverables as part of an agile development team

.Set up model monitoring, logging, and alerting (e.g., drift, latency, failures).

.Ensure model governance, versioning, and traceability across environments.

.Collaborating with others to source, analyse, test and deploy data processes

.Experience in GenAI project

Qualifications we seek in you

Minimum Qualifications

- experience with MLOps practices.

.Degree/qualification in Computer Science or a related field, or equivalent work experience

.Experience developing, testing, and deploying data pipelines

- Strong Python programming skills.

- Hands-on experience in deploying 2 - 3 AI/GenAI models in AWS.

- Familiarity with LLM APIs (e.g., OpenAI, Bedrock) and vector databases.

.Clear and effective communication skills to interact with team members, stakeholders and end users

Preferred Qualifications/ Skills

.Experience with Docker-based deployments.

.Exposure to model monitoring tools (Evidently, CloudWatch).

.Familiarity with RAG stacks or fine-tuning LLMs.

.Understanding of GitOps practices.

.Knowledge of governance and compliance policies, standards, and procedures

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